Modeling and Output Feedback Control of Networked Control Systems with Both Time Delays; and Packet Dropouts

This paper is concerned with the problem of modeling and output feedback controller design for a class of discrete-time networked control systems (NCSs) with time delays and packet dropouts. A Markovian jumping method is proposed to deal with random time delays and packet dropouts. Different from the previous studies on the issue, the characteristics of networked communication delays and packet dropouts can be truly reflected by the unified model; namely, both sensor-to-controller (S-C) and controller-to-actuator (C-A) time delays, and packet dropouts are modeled and their history behavior is described by multiple Markov chains. The resulting closed-loop system is described by a new Markovian jump linear system (MJLS) with Markov delays model. Based on Lyapunov stability theory and linear matrix inequality (LMI) method, sufficient conditions of the stochastic stability and output feedback controller design method for NCSs with random time delays and packet dropouts are presented. A numerical example is given to illustrate the effectiveness of the proposed method

Cloud-based Control of Thermal Based Manufacturing Processes

AbstractWith non-conventional manufacturing processes, being increasingly integrated into manufacturing process chains, controllers and control strategies, remote nowadays, have to take into account a plethora of phenomena and criteria. The current study addresses the challenges, associated with the framework of the thermal oriented processes, having holistic (digital) modelling as a main objective. Herein two different case studies are performed; numerical examples regarding big data impact on manufacturing and simulation-based paradigms of control design taking into account communications. Implementation of the aforementioned takes into account the controller's complexity

H? robust memory controllers for networked control systems: uncertain sampling rates and time delays in polytopic domains

In this paper, the problem of controller design for networked control systems with time-varying sampling rates and time delays is investigated. By using a memory at the feedback loop, a digital robust controller that minimizes an upper bound to the Hinfin performance of the closed loop system is determined. The design conditions are obtained from the Finsler\u27s Lemma combined with the Lyapunov theory and expressed in terms of bilinear matrix inequalities. Extra variables introduced by the Finsler\u27s Lemma are explored in order to provide a better system behavior. The time-varying uncertainties are modelled using polytopic domains. The controller is obtained by the solution of an optimization problem formulated only in terms of the vertices of the polytope, avoiding grids in the parametric space. Numerical examples illustrate the efficiency of the proposed approach

H? robust memory controllers for networked control systems: uncertain sampling rates and time delays in polytopic domains

In this paper, the problem of controller design for networked control systems with time-varying sampling rates and time delays is investigated. By using a memory at the feedback loop, a digital robust controller that minimizes an upper bound to the Hinfin performance of the closed loop system is determined. The design conditions are obtained from the Finsler\u27s Lemma combined with the Lyapunov theory and expressed in terms of bilinear matrix inequalities. Extra variables introduced by the Finsler\u27s Lemma are explored in order to provide a better system behavior. The time-varying uncertainties are modelled using polytopic domains. The controller is obtained by the solution of an optimization problem formulated only in terms of the vertices of the polytope, avoiding grids in the parametric space. Numerical examples illustrate the efficiency of the proposed approach

Active-Varying Sampling-Based Fault Detection Filter Design for Networked Control Systems

This paper is concerned with fault detection filter design for continuous-time networked control systems considering packet dropouts and network-induced delays. The active-varying sampling period method is introduced to establish a new discretized model for the considered networked control systems. The mutually exclusive distribution characteristic of packet dropouts and network-induced delays is made full use of to derive less conservative fault detection filter design criteria. Compared with the fault detection filter design adopting a constant sampling period, the proposed active-varying sampling-based fault detection filter design can improve the sensitivity of the residual signal to faults and shorten the needed time for fault detection. The simulation results illustrate the merits and effectiveness of the proposed fault detection filter design

A Switched Approach to Robust Stabilization of Multiple Coupled Networked Control Systems

This paper proposes a switched approach to robust stabilization of a collection of coupled networked controlled systems (NCSs) with node devices acting over a limited communication channel. We suppose that the state information of every subsystem is split into different packets and only one packet of the subsystem can be transmitted at a time. Multiple NCSs with norm-bounded parameter uncertainties and multiple transmissions are modeled as a periodic switched system in this paper. State feedback controllers can be constructed in terms of linear matrix inequalities. A numerical example is given to show that a collection of uncertain NCSs with the problem of limited communication can be effectively stabilized via the designed controller

Control and filtering of time-varying linear systems via parameter dependent Lyapunov functions

The main contribution of this dissertation is to propose conditions for linear filter and controller design, considering both robust and parameter dependent structures, for discrete time-varying systems. The controllers, or filters, are obtained through the solution of optimization problems, formulated in terms of bilinear matrix inequalities, using a method that alternates convex optimization problems described in terms of linear matrix inequalities. Both affine and multi-affine in different instants of time (path dependent) Lyapunov functions were used to obtain the design conditions, as well as extra variables introduced by the Finsler\u27s lemma. Design problems that take into account an H-infinity guaranteed cost were investigated, providing robustness with respect to unstructured uncertainties. Numerical simulations show the efficiency of the proposed methods in terms of H-infinity performance when compared with other strategies from the literature

Neural Networks: Training and Application to Nonlinear System Identification and Control

This dissertation investigates training neural networks for system identification and classification. The research contains two main contributions as follow:1. Reducing number of hidden layer nodes using a feedforward componentThis research reduces the number of hidden layer nodes and training time of neural networks to make them more suited to online identification and control applications by adding a parallel feedforward component. Implementing the feedforward component with a wavelet neural network and an echo state network provides good models for nonlinear systems.The wavelet neural network with feedforward component along with model predictive controller can reliably identify and control a seismically isolated structure during earthquake. The network model provides the predictions for model predictive control. Simulations of a 5-story seismically isolated structure with conventional lead-rubber bearings showed significant reductions of all response amplitudes for both near-field (pulse) and far-field ground motions, including reduced deformations along with corresponding reduction in acceleration response. The controller effectively regulated the apparent stiffness at the isolation level. The approach is also applied to the online identification and control of an unmanned vehicle. Lyapunov theory is used to prove the stability of the wavelet neural network and the model predictive controller. 2. Training neural networks using trajectory based optimization approachesTraining neural networks is a nonlinear non-convex optimization problem to determine the weights of the neural network. Traditional training algorithms can be inefficient and can get trapped in local minima. Two global optimization approaches are adapted to train neural networks and avoid the local minima problem. Lyapunov theory is used to prove the stability of the proposed methodology and its convergence in the presence of measurement errors. The first approach transforms the constraint satisfaction problem into unconstrained optimization. The constraints define a quotient gradient system (QGS) whose stable equilibrium points are local minima of the unconstrained optimization. The QGS is integrated to determine local minima and the local minimum with the best generalization performance is chosen as the optimal solution. The second approach uses the QGS together with a projected gradient system (PGS). The PGS is a nonlinear dynamical system, defined based on the optimization problem that searches the components of the feasible region for solutions. Lyapunov theory is used to prove the stability of PGS and QGS and their stability under presence of measurement noise